Antibiotic resistance is a growing problem in the world today. Antibiotic resistant strains of pathogenic bacteria emerge every day and represent a significant health care challenge According to Science, in 1980 around 1% to 5% of S. aureus was methicillin resistant and today 60% to 70% of S. aureus strains found in hospitals are methicillin resistant. This alarming increase in bacterial resistance to antibiotics has motivated an active search for novel viable targets for antibiotic drug design.
Publication Nos. US 2002/0052694 and 2002/0077754 discloses a specialized apparatus and methods for identifying, representing, and productively using high activity regions of chemical structure space. At least two representations of chemical structure space provide valuable information. A first representation has many dimensions representing members of a pharmacophore basis set and one or more additional dimensions representing defined chemical activity (e.g., pharmacological activity). A second representation has many fewer dimensions, each of which represents a principle component obtained by transforming the first representation via principal component analysis used on pharmacophore fingerprint/activity data for a collection of compounds. When the collection of compounds has the defined chemical activity, that activity will be reflected as a “high activity” region of chemical space in the second representation.
Publication No. 2005/0009093 discloses a method for generating a focused compound library containing an enriched amount of ligand compounds being capable of binding to a predetermined receptor.
Publication No. US 2005/0049794 discloses a processes for producing an optimized pharmacophore for a target protein. The invention also relates to processes for identifying compounds having an affinity to a target protein. The invention also relates to processes for designing a ligand for a target protein using the optimized pharmacophore of the present invention. The invention also provides a computer for use in designing a ligand for a target protein using the optimized pharmacophore of the present invention.
Publication No. 2005/0053978 discloses methods and systems for generating pharmacophore models characterized both by molecular features that are present in the model and features that are defined as absent. Thus, models can be developed that take into account features such as steric bulk that inhibit activity for a specified target as well as features such as functional groups that promote activity. Features that inhibit activity can be identified by comparing known active molecules with known inactive molecules. Features that are present in the inactive molecules but absent in the active molecules can be defined in a pharmacophore model.
Publication No. 2005/0177318 discloses pharmacophores in molecules identified by generating a set of conformations for a respective molecule. A respective conformation includes a series of features that are present or absent in the conformation, wherein a respective feature includes at least two molecular elements and at least one distance between the molecular elements. The features for a set of conformations for a given molecule are repeatedly compared to a model of feature importance of remaining molecules, to identify an inferred conformation of a given molecule, until the model of feature importance for the molecules converges.
Publication No. US 2006/0206269 discloses a set of molecules, the members of which have the same type of biological activity, represented as one-dimensional strings of atoms. The one-dimensional strings of all members of the set are aligned, in order to obtain a multiple alignment profile of a consensus active compound. The one-dimensional multiple alignment profile is used in deriving a one-dimensional QSAR model to identify other compounds likely to have the same biological activity, and also may be used to derive a three-dimensional multiple alignment profile of the molecules in the set.
Publication No. US 2007/0156343 discloses a stochastic algorithm for predicting the drug-likeness of molecules. It is based on optimization of ranges for a set of descriptors. Lipinski's “rule-of-5”, which takes into account molecular weight, log P, and the number of hydrogen bond donor and acceptor groups for determining bioavailability, was previously unable to distinguish between drugs and non-drugs with its original set of ranges. The invention demonstrates the predictive power of the stochastic approach to differentiate between drugs and non-drugs using only the same four descriptors of Lipinski, but modifying their ranges. However, there are better sets of 4 descriptors to differentiate between drugs and non-drugs, as many other sets of descriptors were obtained by the stochastic algorithm with more predictive power to differentiate between databases (drugs and non-drugs). A set of optimized ranges constitutes a “filter”. In addition to the “best” filter, additional filters (composed of different sets of descriptors) are used that allow a new definition of “drug-like” character by combining them into a “drug like index” or DLI. In addition to producing a DLI (drug-like index), which permits discrimination between populations of drug-like and non-drug-like molecules, the present invention may be extended to be combined with other known drug screening or optimizing methods, including but not limited to, high-throughput screening, combinatorial chemistry, scaffold prioritization and docking.
Publication No. US 2007/0198195 discloses a computational method of determining a set of proposed pharmacophore features describing interactions between a known biological target and known training ligands that show activity towards the biological target.
The identification of potentially novel drugs and molecular targets can assist in preventing antibiotic resistance. Bacterial peptidoglycan biosynthesis is a well validated and a very attractive target for the design and discovery of new antibacterial agents since it is unique to bacteria cells (does not occur in humans) and are unexploited steps in the pathway. Currently, several bactericidal antibiotics available on the market target the bacterial peptidoglycan biosynthesis pathway, e.g., vancomycin. However, these agents are highly susceptible to resistance.
A new drug target in the peptidoglycan biosynthetic pathway is glutamate racemase (glu racemase), an enzyme which catalyses the conversion of L-glutamate to D-glutamate providing D-glutamate for peptidoglycan biosynthesis. Knock-out mutations have shown the glutamate racemase gene to be essential in Escherichia coli (E. coli) and S. pneumoniae. Recently, a group of glutamate racemase inhibitors were developed1 through chemical synthesis but enthusiasm for these agents waned as they possessed a narrow spectrum of antibacterial activity against only S. pneumoniae. The apparent poor antibacterial activity of these compounds was due in part to poor membrane permeability. 1 de Dios A, Prieto L, Martin J A, et al. 4-substituted D-glutamic acid analogues: The first potent inhibitors of glutamate racemase (MurI) enzyme with antibacterial activity. J Med. Chem. 2002; 45:4559-4570
Therefore, a drawback of known glutamate racemase inhibitors is their poor lipophilic nature. It was hypothesized that the charged groups in the D-glu-analogue inhibitors2 make them poorly lipophilic and unable to permeate through biological membranes. The minimum inhibitory concentration (MIC) from whole-cell assays of some of these inhibitors did not correlate with their IC50 values from the in vitro enzyme assays further supporting this hypothesis. In addition, the poor lipophilic nature of these inhibitors makes them poor drug candidates as they will show poor gut permeability and poor absorption from the intestine. 2 Id.
Accordingly, there remains a need for glutamate racemase inhibitors with enhanced lipophilic properties. Eliminating some or all of the charged groups enhances the lipophilic nature of these inhibitors and, as a consequence, enhances their membrane permeability properties which in turn enhances not only their antibacterial spectrum but their pharmacokinetic profile as well. However, those charged groups may be essential for binding and inhibition of the enzyme. The present invention is directed to a method of enhancing the pharmacokinetic profile of the charged poorly lipophilic glu racemase inhibitors while preserving their antibacterial activity using a ligand-based drug design approach.